Big Data @ SAS IFSUG
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Transcript of Big Data @ SAS IFSUG
Insurance Telematics: Big Data, Big Potential, Big Headache
Dave Huber, President
Kairos Solutions
IFSUG March 2012
One of the few products whose price is set before costs are known
Known costs Unknown costs
3
O Loss adjustment expense
O Operations
O Advertising
O Underwriting
O Commissions
O Pure premium (freq x sev)
O Bodily injury
O Comp & Collision
O Regulatory
O Trends
Known costs
Unknown costs Premium
Data drives insurance decisions
Pricing sophistication is a competitive advantage and depends on data analytics
O Granularity O The number of pricing cells per question or variable
O Age: 16-19, 20-25, 26-30…vs. 16, 17, 18, 19….
O Dispersion O The range of rates for each of the variables
O $450-$900 vs. $225-$1375
O Interactions O The lift when combining variables
O Vehicle symbol & territory – pickups in suburbs
O Variables O New questions and/or external data
O Credit, occupation, prior limits
4
Insurers generally use the same data to price
5
Age
Gender
Marital status
Violations
Points
Homeowner
Prior insurance
Credit
Vehicle
31
M
S
Speed
4
Own
Y
611
YMM
31
M
S
Speed
4
Own
Y
611
YMM
These drivers look like Pure Premium Carbon Copies and are priced identically
$1000 $1000
But imagine knowing something about drivers that no one else knows
6
31
M
S
Speed
4
Own
Y
611
YMM
10,651
4.9
31
M
S
Speed
4
Own
Y
611
YMM
13,182
6.1
$800 $1200
Age
Gender
Marital status
Violations
Points
Homeowner
Prior insurance
Credit
Vehicle
Verified Annual Miles
Trips per day
So they’re NOT Pure Premium Carbon Copies after all…and they deserve a different price
Usage-Based Insurance is all about segmentation & pricing
O How, when & where you drive
O Driving data’s not readily available &
expensive to collect
O Need a lot of driving data
O Beyond insurers’ core competency
O Insurers would really like a driving score
7
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The pricing advantage of UBI data is big
O Granularity O The number of pricing cells per question or variable
O Age: 16-19, 20-25, 26-30…vs. 16, 17, 18, 19….
O Self-reported mileage buckets vs. verified continuous mileage
O Variables O New questions and/or external data
O Credit, occupation, prior limits
O How, when & where, self-selection, personal driving score akin to a credit score
O Interactions O The lift when combining variables
O Vehicle symbol & territory – pickups in suburbs
O Miles x time of day, frequency & magnitude of speed changes, speed x traffic
O Dispersion O The range of rates for each of the variables
O $450-$900 vs. $225-$1375
O Personalized pricing
How big is Big Data?
O Time-stamped trip start/stop, engine on/off
O OBD - vehicle speed every second
O GPS - lat, long & heading every second
O Accelerometer – 3 axis acceleration
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O 5,000 GPS-enabled devices
O 8MM journeys & 15B journey points
O 20 million new rows of data daily
So what does when, where & how look like?
How might all this Big Data show up?
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Annual mileage
Avg trip duration
Avg trip length
Trips per day
Trips per time of day
Journeys
Miles by time of day
Miles by day of week
Weekdays
Weekends
Miles in speed bands
Time in speed bands
Average speed
Trip regularity (miles)
Trip regularity (time)
Aggressive acceleration per 100 miles
Aggressive braking per 100 miles
Road type
Relative speed
Miles in territory
Drive time in territory
Idle time in territory
Cornering
Lateral acceleration
Rolling stops
Self-selection
Lane changes
Acceleration events in speed bands
Braking events in speed bands
Frequency of speed changes
Magnitude of speed changes
Commuter profile
Errand-runner profile
Coffee drinkers
YMM relativities
OnStar subscription
Cruise control
Driver score
Driver “footprint”
Left turns
Speed variation
Trip type (speed vs time)
Territory by time of day
Holiday driving
School zone
Violations by trip type
Trip radius
Student profile
Intersections
Turn signal
Seat belt
Lights / wipers
Vehicle maintenance
Time between trips/journeys
Congestion index
Summer car
Texting & phone use